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10/13/2026 | Press release | Distributed by Public on 10/13/2026 13:01

Jamming the Flow to Enable Memory in Quantum Materials

Published Date

October 13, 2026

Article Content

Understanding and controlling phase transitions is fundamental to physics and central to many technological revolutions. There is currently strong interest in materials with coupled structural and electronic phase transitions, where controlling the transition plasticity naturally yields built-in memory - a key step in emulating neurons and synapses for "neuromorphic" or brain-like computing technologies.

A team of UC San Diego researchers led by Professor of Physics Oleg Shpyrko developed a new approach to coherent X-ray diffraction - a microscopy technique that is highly sensitive to specific nanoscale domain configuration - that allowed them to observe dynamics within a neuristor (a manmade device that mimics the brain's neurons) made of vanadium dioxide while electricity was applied. In doing the measurements while operating the neuristor, they discovered a strategy to make the material learn and forget a specific state, opening an avenue toward developing energy-efficient brain-inspired hardware.

The physical phenomenon which enables this control is a phase transition from a liquid-like state to a jammed (or glassy) state. This is somewhat similar to passengers waiting for a train. When there are few people on the platform, they can move independently of each other. When the platform is crowded, the movements of one person can affect others. In the case of the neuristor, by jamming the flow learning is enabled, and by restoring free-flow memory is erased.

The findings have practical implications for the development of energy-efficient neuromorphic devices and for fundamental materials science. Finding a way to control how neuristors can "learn" and "forget" is central to the operation of neuromorphic devices.

The study was published September 24, 2026 in Nature Communications. UC San Diego authors include Nicolò D'Anna, Nareg Ghazikhanian, Katherine Matthews, Alex Frano, Ivan K. Schuller and Oleg G. Shpyrko. Their research was supported by Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C), an Energy Frontier Research Center funded by the US Department of Energy (DOE), Office of Science, Basic Energy Sciences (DE-SC0019273).

Read the study in Nature Communications: "Jamming-controlled stochasticity in metal-insulator switching."

Learn more about research and education at UC San Diego in: Artificial Intelligence

Read more news about: Physical Sciences, Artificial Intelligence

"The research provides an exciting avenue toward developing energy-efficient devices that mimic neurons that can learn and forget at the device level." -- Nicolò D'Anna, postdoctoral researcher
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